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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Joanna Koszyk Łukasz Ambroziński Piotr Łabędź Department of Robotics and Mechatronics AGH University of Krakow Krakow Poland Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In the field of mobile robotics, point clouds are widely used as an environment representation merged into a map. However, point cloud registration can be challenging when rapid movement occurs. In indoor environments, planelike features such as walls, floors, and ceilings are dominant and can be used to aid cloud merging. These features need to be extracted from point clouds. In this paper, we propose an open-source Hough transform implementation in Python. We have adapted this method, conventionally used for detecting shapes in images, to be applied to 3D point clouds and to detect planar features. To illustrate the point-cloud Hough transform algorithm, we generated synthetic point clouds. Additionally, we collected LiDAR measurements in the corridor. Processing the experimental data proved the algorithm’s ability to handle noise and measurement uncertainties. The study included an evaluation of the Hough transform by making a comparison to the ICP method. The Hough Transform provided more accurate results of corridor width measured on a merged point cloud by 0.06 m.

关键词： Point cloud compression Performance evaluation Accuracy Three-dimensional displays Simultaneous localization and mapping Shape Noise

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Matching Input and Output Devices and Physical Disabilities for Human-Robot Workstations

Matching Input and Output Devices and Physical Disabilities ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Carlo Weidemann Nils Mandischer Burkhard Corves Institute of Mechanism Theory Machine Dynamics and Robotics RWTH Aachen University Aachen Germany Chair of Mechatronics University of Augsburg Augsburg Germany

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

As labor shortage is rising at an alarming rate, it is imperative to enable all people to work, particularly people with disabilities and elderly people. Robots are often used as universal tool to assist people with disabilities. However, for such human-robot workstations universal design fails. We mitigate the challenges of selecting an individualized set of input and output devices by matching devices required by the work process and individual disabilities adhering to the Convention on the Rights of Persons with Disabilities passed by the United Nations. The objective is to facilitate economically viable work-stations with just the required devices, hence, lowering overall cost of corporate inclusion and during redesign of workplaces. Our work focuses on developing an efficient approach to filter input and output devices based on a person's disabilities, resulting in a tailored list of usable devices. The methodology enables an automated assessment of devices compatible with specific disabilities defined in International Classification of Functioning, Disability and Health. In a mock-up, we showcase the synthesis of input and output devices from disabilities, thereby providing a practical tool for selecting devices for individuals with disabilities.

关键词： Impedance matching Employment Decision making People with disabilities Workstations Object recognition Older adults Robots Cybernetics Graphical user interfaces

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Speeding Up Optimization-based Motion Planning through Deep Learning

arXiv

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arXiv 2023年

作者： Tenhumberg, Johannes Burschka, Darius Bäuml, Berthold DLR Institute of Robotics & Mechatronics Germany Deggendorf Institute of Technology Germany Technical University of Munich Germany

Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding up the planning by encoding prior experience of successful motion plans in a neural network. However, this "neural motion planning" did not scale to complex robots in unseen 3D environments as needed for real-world applications. Here, we introduce "basis point set", well-known in computer vision, to neural motion planning as a modern compact environment encoding enabling efficient supervised training networks that generalize well over diverse 3D worlds. Combined with a new elaborate training scheme, we reach a planning success rate of 100 %. We use the network to predict an educated initial guess for an optimization-based planner (OMP), which quickly converges to a feasible solution, massively outperforming random multi-starts when tested on previously unseen environments. For the DLR humanoid Agile Justin with 19 DoF and in challenging obstacle environments, optimal paths can be generated in 200 ms using only a single CPU core. We also show a first successful real-world experiment based on a high-resolution world model from an integrated 3D sensor. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Conceptualization of a Hybrid Machine Combining Laser Beam Micro Machining (LBMM) and Micro Electrical Discharge Machining (Micro-EDM) in a Single Setup

Conceptualization of a Hybrid Machine Combining Laser Beam M...

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International Conference on mechatronics (ICOM)

作者： Wan Ahmad Bin Wan Azhar Tanveer Saleh Autonomous Systems and Robotics Research Unit Department of Mechatronics Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur Malaysia

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

The utilization of hybrid laser beam micro machining (LBMM) in conjunction with micro electrical discharge machining $(\mu\text{EDM})$ has gained widespread popularity due to its ability to improve material removal rates and enhance surface quality. Typically, these processes occur on separate machines, resulting in drawbacks such as time loss during workpiece transfer, potential misalignment issues, and increased risks of sample damage during the transfer. This research provides a system and method for a two-step sequential machining process of LBMM and $\mu\text{EDM}$ that allows the workpiece to remain at the same location for both processes. A LBMM process is used for drilling a rough blind hole, followed by $\mu\text{EDM}$ for the finishing process. The workpiece is fixed and positioned in the $\mu\text{EDM}$ workstation. A microscopic camera, which is mounted on the $\mu\text{EDM}$ machine, is used to provide common coordinate position data for the laser and $\mu\text{EDM}$ machine. This coordinate is used by the $\mu\text{EDM}$ machine to position the electrode perfectly on top of LBMM hole before machining. The results suggest that LBMM- $\mu\text{EDM}$ machining can reduce machining time while achieving good hole quality and repeatability. The hybrid sequential process significantly reduces drilling time, being about 1.55 times faster than the pure $\mu\text{EDM}$ [method. However, the findings also indicate that it is not necessary to use LBMM- $\mu\text{pEDM}$ for low aspect ratio holes. This concept enables the workpiece to stay in one position throughout both processes, eliminating any time loss and damage during transfer, as well as avoiding misalignment issues.

关键词： Drilling mechatronics Surface discharges Microscopy Surface emitting lasers Machining Micromachining

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Open-source objective-oriented framework for head-related transfer function

arXiv

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arXiv 2022年

作者： Szwajcowski, Adam AGH University of Science and Technology Department of Robotics and Mechatronics Poland

Throughout last 30 years, numerous head-related transfer function (HRTF) models have been developed and there are more to come. This paper describes a framework based on objective-oriented programming paradigm, in which each HRTF representation method can be implemented as a separate class. Its modular structure allows the source code to be conveniently shared between researchers, while common interface provides easy access to data regardless of the internal structure of the classes. The paper discusses difficulties of designing the framework, maintaining the balance between its flexibility and finding common features of every possible directivity representation. Exemplary use cases are included and explained. Adoption of the framework will enhance possibilities of accuracy comparison between various HRTF models, thus improving the evaluation of current and future representation methods. The framework, developed in the form of a MATLAB toolbox, is designed to handle not only HRTFs but also other types of spatial data, such as e.g. sound source directivity, microphone directivity, etc. © 2022, CC BY.

关键词： MATLAB

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ROSMC: A High-Level Mission Operation Framework for Heterogeneous Robotic Teams

ROSMC: A High-Level Mission Operation Framework for Heteroge...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ryo Sakagami Sebastian G. Brunner Andreas Dömel Armin Wedler Freek Stulp DLR (German Aerospace Center) Institute of Robotics and Mechatronics Wessling Germany At DLR at the time of development of ROSMC

Heterogeneous teams of multiple mobile robots will be important for future scientific explorations of extraterrestrial surfaces or hazardous areas. Mission operation in such harsh, unknown environments poses diverse challenges. Robots need to cooperate autonomously due to the large network latency to the ground station while operators need to adapt the ongoing mission flexibly based on new discoveries obtained during execution. Furthermore, shared situational awareness between operators and roboticists is highly required to deal with execution failures promptly. To overcome these challenges, this paper proposes the high-level mission operation framework ROSMC. The concept of mission synchronization to robots enables continuous mission adaptations and future planning by operators while robots execute the mission autonomously. The ROS-based GUIs enable operators to intuitively create and monitor the mission for robots as well as to communicate with roboticists smoothly. The proposed framework was evaluated by a pilot study with a simulator and demonstrated at a Moon-analogue field on Mt. Etna in Sicily, Italy, involving 3 robots and around 70 researchers for 4 weeks.

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Fuzzy Feedback Control for Electro-Hydraulic Actuators

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Intelligent Automation & Soft Computing 2023年第5期36卷 2441-2456页

作者： Tan Nguyen Van Huy Q.Tran Vinh Xuan Ha Cheolkeun Ha Phu Huynh Minh School of Engineering-Technology Thu Dau Mot UniversityThu Dau Mot City590000Vietnam Faculty of Engineering-Technology Nguyen Tat Thanh UniversityHo Chi Minh City754000Vietnam Hue Industrial College Hue City530000Vietnam Robotics and Mechatronics Lab University of UlsanUlsan City44610Korea

Electro-hydraulic actuators(EHA)have recently played a signiﬁcant role in modern industrial applications,especially in systems requiring extremely high *** can be explained by EHA’s ability to precisely control the position and force through advanced sensors and innovative control *** of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms,in which the proportional–inte-gral–derivative(PID)algorithm,fuzzy logic controller,and a hybrid of these methods are popular *** this paper,we developed a novel version of the fuzzy control algorithm and linear feedback control method,namely fuzzy lin-ear feedback control,to improve the control *** achieve the highest performance,weﬁrst designed a mathematical EHA model based on the Matlab/Simulink software packages thanks to the selected parameters,which are similar to a real EHA ***,we respectively applied PID,fuzzy PID(FPID),and fuzzy linear feedback control(FLFC)before comparing them to have a full view of the outstanding advantages of the proposed *** simulation results showed that the proposed FLFC algorithm is approximately 99%and 77%super-ior in performance to the PID and feedback control algorithms,respectively.

关键词： Electro-hydraulic actuator fuzzy PID feedback control

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Adaptive Filter for Biosignal-Driven Force Controls Preserves Predictive Powers of sEMG

Adaptive Filter for Biosignal-Driven Force Controls Preserve...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Marek Sierotowicz Marc-Anton Scheidl Claudio Castellini Institute of Assistive Intelligent Robotics Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany Institute of Robotics and Mechatronics German Aerospace Center Wessling Germany

Electromyographic controls based on machine learning rely on the stability and repeatability of signals related to muscular activity. However, such algorithms are prone to several issues, making them non-viable in certain applications with low tolerances for delays and signal instability, such as exoskeleton control or teleimpedance. These issues can become dramatic whenever, e.g., muscular activity is present not only when the user is trying to move but also for mere gravity compensation, which generally becomes more prominent the more proximal a muscle is. A substantial part of this instability is attributed to electromyography's inherent heteroscedasticity. In this study, we introduce and characterize an adaptive filter for sEMG features in such applications, which automatically adjusts its own cutoff frequency to suit the current movement intention. The adaptive filter is tested offline and online on a regression-based joint torque predictor. Both the offline and the online test show that the adaptive filter leads to more accurate prediction in terms of root mean square error when compared to the unfiltered prediction and higher responsiveness of the signal in terms of lag when compared to the output of a conventional low-pass filter.

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Modular Multi-axis Elastic Actuator with Torque Sensing Capable p-CFH for Highly Impact Resistive Robot Leg

Modular Multi-axis Elastic Actuator with Torque Sensing Capa...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Youngrae Kim Sunghyun Choi Jinhyeok Song Dongwon Yun Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea

This study proposes a modular Multi-axis Elastic Actuator (MAEA) for legged robots that can effectively cope with impacts that may occur during dynamic maneuvering. MAEA has multi-axis compliance and can measure the torque without additional encoders. Therefore, effective impact resistance is possible with less volume and weight than conventional Series Elastic Actuators (SEA). The 6-axis stiffness analysis of paired-Crossed Flexural Hinge (p-CFH) is extended from small deformation to large deformation, and the accuracy is verified through Finite Element Analysis (FEA) and experiments. Based on the analysis, the torque of p-CFH is measured, and feedback torque control is also performed. Finally, the robot leg was constructed with MAEA, and the multi-axis impact resistance performance of MAEA was demonstrated by analyzing the applied impact during landing experiments at various angles.

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i-Glove: An Intelligent Glove System Based on Deep Learning to Support Deaf-blind Individuals in Recognizing Banknotes

i-Glove: An Intelligent Glove System Based on Deep Learning ...

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robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Fahad Mohammad Hossain Rafit Alavi Yeamin Safat Gobinda Chandra Sarker Nafisa Noor Ekram Hossain Department of Computer Science and Engineering Mymensingh Engineering College Mymensingh Bangladesh Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

The deaf-blind community faces significant challenges in identifying banknotes due to simultaneous hearing and vision impairments, which complicate their ability to conduct daily financial transactions. Recent advances in Artificial Intelligence (AI)-based assistive technologies offer promising potential to address these challenges. This study presents the "i-Glove," a wearable device specifically developed to assist the deaf-blind community in Bangladesh with accurate banknote identification. The i-Glove is designed to recognize nine common Bangladeshi banknotes (e.g., BDT 20, 50, 100) through a camera module integrated on the palmar side of the glove, which captures real-time images of the banknotes. These images are then processed by a convolutional neural network (CNN) model to accurately classify each banknote. Identified notes are translated into unique vibration patterns that are delivered to the user through a shaftless vibration motor embedded in the glove, providing intuitive, non-visual communication. Compared to previous approaches, our model demonstrates superior performance even with a compact dataset, achieving an overall F1-score accuracy of 96.55%, with class-specific precision, recall, and F1-score metrics ranging from 90% to 100%. Additionally, a dedicated application is developed which supports real-time banknote detection and interfaces seamlessly with the glove. The proposed system represents a significant step forward in assistive technology for the deaf-blind community, empowering the users with greater autonomy in financial transactions.

关键词： Vibrations Visualization Accuracy Image recognition Tactile sensors Assistive technologies Motors Real-time systems Convolutional neural networks Wearable devices

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